Subsurface Oxygen \(O_2\)#
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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize
sys.path.append("../../../functions")
from data_downloaders import download_oni_index
Setup#
Define area of interest
#Area of interest
lon_range = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]
EEZ shapefile
path_figs = "../../../matrix_cc/figures"
shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)
Load Data#
Superficial depth
data_xr = xr.open_dataset(op.join(os.getcwd(), '..', '..','..', 'data/data_phyc_o2_ph.nc')).isel(depth = 0)
dataset_id = 'o2'
label = 'O2 [µmol/kg]'
Analysis#
Plotting#
Average#
ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(),
cmap = 'BuPu',
vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1),
vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label= label)
plt.savefig(op.join(path_figs, 'F17_O2_mean_map.png'), dpi=300, bbox_inches='tight')
Annual average#
data_y = data_xr.resample(time='1YE').mean()
im = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'BuPu',
vmin = np.nanpercentile(data_xr.min(dim = 'time')[dataset_id], 1),
vmax = np.nanpercentile(data_xr.max(dim = 'time')[dataset_id], 99),
cbar = 1)
Annual anomaly#
data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-4, vmax=4, cbar = 1)
Average over area#
dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(),
'var' : dataset_id, 'ax' : 1, 'label' : f'{label} - MEAN AREA'},]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = label);
fig.write_html(op.join(path_figs, 'F17_O2_mean_trend.html'), include_plotlyjs="cdn")
Top 10 years#
data_mean = data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe()
data_mean = data_mean.resample('YE').mean()
top_10 = data_mean.sort_values(by='o2', ascending=False).head(10)
fig, ax, trend = plot_bar_probs(x=data_mean.index.year, y=data_mean.o2, trendline=True,
y_label='Dissolved O2', figsize=[15, 4], return_trend=True)
ax.set_ylim(data_mean.o2.min()-0.5, data_mean.o2.max()+0.5)
im = ax.scatter(top_10.index.year, top_10.o2,
c=top_10.o2.values, s=100, ec='pink', cmap='rainbow', label='Top 10 warmest years')
plt.colorbar(im, pad=0, shrink=.7).set_label('Absolute Dissolved O2', fontsize=fontsize)
ax.set_title('Annual Mean Dissolved O2', fontsize=15);
Timeseries at a given point#
loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(),
'var' : dataset_id, 'ax' : 1, 'label' : f'{label} at [{loc[0]}, {loc[1]}]'},]
ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()
<matplotlib.legend.Legend at 0x1828eecc0>
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = label);
ONI index analysis#
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
Group by ONI category
df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()
Average#
fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'BuPu',
vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1) - 1,
vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99) + 1,
sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F17_O2_ENSO.png'), dpi=300, bbox_inches='tight')
Anomaly#
data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-1, vmax=1,
sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
titles = ['La Niña', 'Neutral', 'El Niño'],)
